Federal and State Tax law has become so complex that it is now estimated that each year Americans alone use over 6 billion person hours, and spend nearly 4 billion dollars, in an effort to comply with Federal and State Tax statutes. Given this level of complexity and cost, it is not surprising that more and more taxpayers find it necessary to obtain help, in one form or another, to prepare their taxes. Tax return preparation systems, such as tax return preparation software programs and applications, represent a potentially flexible, highly accessible, and affordable source of tax preparation assistance. However, traditional tax return preparation systems are, by design, fairly generic in nature and often lack the malleability to meet the specific needs of a given user.
For instance, traditional tax return preparation systems often present a fixed, e.g., predetermined and pre-packaged, structure or sequence of questions and tax return amounts to all users as part of the tax return preparation interview process. This is largely due to the fact that the traditional tax return preparation system analytics use a sequence of interview questions, and/or other user experiences, that are static features and that are typically hard-coded elements of the tax return preparation system and do not lend themselves to effective or efficient modification. As a result, the user experience, and any analysis associated with the interview process and user experience, is a largely inflexible component of a given version of the tax return preparation system. There is therefore little or no opportunity for any analytics associated with the interview process, and/or user experience, to evolve to meet a changing situation or the particular needs of a given taxpayer, even as more information about that taxpayer, and their particular circumstances, is obtained.
As an example, many users who file early in the tax season are filing because they expect a tax return. To these users, the most important information of the entire interview process is the dollar amount they will receive back from the government. However, to their dismay, traditional tax return preparation systems string the user along through a seemingly endless question and answer session before ever providing even partial tax return dollar amounts. Since the motivation of the user for initiating the tax return preparation process is to find out how much the user can expect or estimate to get back, it can be extraordinarily frustrating to have to wait until half way or longer through the interview session before receiving an indication of the amount of their tax return.
What is needed is a method and system for applying analytics models to a tax return preparation system to determine a likelihood of qualification for earned income tax credit by a user, to reduce the delays in presenting earned income tax credit benefits to the user during a tax return preparation session, and to reduce a likelihood of abandonment of a tax return preparation session, for example, due to feelings of frustration.